Efficiently planning and organizing large events relies on methods of collecting event-related data regarding many aspects of the activity surrounding and within the event or events. Such datasets are rich in information and have consequently attracted much attention in disciplines relating to data analytics and data mining. These datasets can be mined and analyzed to enable host organizers to more fully prepare for the event. This information can also assist employers and institutions wishing to participate in the event/events. The information collected can be used to maximize participant experience as well as determine the advantages of participation. Generally, an event dataset can be regarded as being indicative of future attributes of any given event from a starting point to an ending point, wherein the data points collected can refer to any physical or other entity describing the event defined by essentially any physical or other parameter. By using patterns of past event data sets regarding organization and planning, new subsets of data can be determined for future events.
Generally, in analyzing data subsets, very different subset trees can be generated for different types of events and samples recorded for each said event. Many different trees can be found in a variety of sample data and can be used to extrapolate information based on tree relationships. Ontology information based on tree relationships can be generated for classification used for data mining. An ontological structure in the form of a data tree can be defined as a structure containing multiple branches of data in which each branch can trace its beginning to a particular event in this case, and although there may be many branches of the tree, each branch may be unrelated except for the parent event. Using different tree ontologies can lead to the creation of event models for handling different attribute specific matching functions, such as the determination of missing data attributes. A missing data attribute is defined as an observation (or set of observations) that can be resolved by using models for predictive classification within the rest of the data ontology (e.g., with respect to a predetermined data point or points); thus, a missing attribute represents a category or sub-category that can be found by using matching functions. This can amount to pinpointing one or more categories that qualify as missing in the context of one or more data subset trees. Challenges continue to be encountered in efficiently finding and designating such missing attributes, especially missing attributes of events to be planned, and viable, cost-effective solutions continue to elude event organizers.